Cohort Profile: The Ageing Trajectories of Health – Longitudinal Opportunities and Synergies (ATHLOS) project

Sánchez-Niubò, Albert; Egea‐Cortés, Laia; Olaya, Beatriz; Caballero, Francisco Félix; Ayuso-Mateos, José Luís; Prina, Matthew; Bobák, Martin; Arndt, Holger; Tobiasz–Adamczyk, Beata; Pająk, Andrzej; Leonardi, Matilde; Koupil, Ilona; Panagiotakos, Demosthenes B.; Tamošiūnas, Abdonas; Scherbov, Sergei; Sanderson, Warren C.; Koskinen, Seppo; Chatterji, Somnath; Haro, Josep María

doi:10.1093/ije/dyz077

Cited by 40 publications

(60 citation statements)

References 26 publications

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“…This study was based on the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project (http://athlosproject.eu/). The aim of the ATHLOS project was to harmonise data from 17 international ageing cohort studies [13] and to investigate trajectories of healthy ageing and their potential determinants in different older populations. Data harmonisation is a technique where data from different studies and with different format are transformed and merged together in order to produce one cohesive dataset.…”

Section: Study Samplementioning

confidence: 99%

“…Harmonisation of variables across studies was based on an iterative process of consensus of experts, which is transparently documented and publicly available harmonisation reports (https://github.com/athlosproject/athlos-project.github.io/). Further details on the included cohorts and the harmonised variables is also available in the cohort profile of the ATHLOS project [13]. To carry out longitudinal analyses and identify different trajectories of healthy ageing, we focused on cohorts with three or more available waves.…”

Section: Study Samplementioning

confidence: 99%

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The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies

Moreno-Agostino

Daskalopoulou

et al. 2020

Int J Behav Nutr Phys Act

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107

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Background: Research has suggested the positive impact of physical activity on health and wellbeing in older age, yet few studies have investigated the associations between physical activity and heterogeneous trajectories of healthy ageing. We aimed to identify how physical activity can influence healthy ageing trajectories using a harmonised dataset of eight ageing cohorts across the world. Methods: Based on a harmonised dataset of eight ageing cohorts in Australia, USA, Mexico, Japan, South Korea, and Europe, comprising 130,521 older adults (M age = 62.81, SD age = 10.06) followed-up up to 10 years (M follow-up = 5.47, SD follow-up = 3.22), we employed growth mixture modelling to identify latent classes of people with different trajectories of healthy ageing scores, which incorporated 41 items of health and functioning. Multinomial logistic regression modelling was used to investigate the associations between physical activity and different types of trajectories adjusting for sociodemographic characteristics and other lifestyle behaviours. Results: Three latent classes of healthy ageing trajectories were identified: two with stable trajectories with high (71.4%) or low (25.2%) starting points and one with a high starting point but a fast decline over time (3.4%). Engagement in any level of physical activity was associated with decreased odds of being in the low stable (OR: 0.18; 95% CI: 0.17, 0.19) and fast decline trajectories groups (OR: 0.44; 95% CI: 0.39, 0.50) compared to the high stable trajectory group. These results were replicated with alternative physical activity operationalisations, as well as in sensitivity analyses using reduced samples. Conclusions: Our findings suggest a positive impact of physical activity on healthy ageing, attenuating declines in health and functioning. Physical activity promotion should be a key focus of healthy ageing policies to prevent disability and fast deterioration in health.

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Section: Study Samplementioning

confidence: 99%

Section: Study Samplementioning

confidence: 99%

The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies

Moreno-Agostino

Daskalopoulou

et al. 2020

Int J Behav Nutr Phys Act

Self Cite

107

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“…The present study used data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. 16 Longitudinal data from 17 international cohort studies related to health and ageing were harmonised with the aim of obtaining an integrated dataset and achieving a better understanding of ageing and health processes.…”

Section: Study Design and Data Extractionmentioning

confidence: 99%

Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts

et al. 2020

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ObjectivesOur aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates.DesignThe total sample was 72 140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years old).ResultsThree clusters were identified: ‘cardio-metabolic’ (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), ‘respiratory-mental-articular’ (3.91% and 5.27%) and ‘healthy’ (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the ‘cardio-metabolic’ group (18.8%) and England the ‘respiratory-mental-articular’ (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the ‘healthy’ class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the ‘respiratory-mental-articular’.ConclusionMultimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity.

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“…The overall follow‐up period was from 1995 to 2013. Further information on studies in ATHLOS can be found on the ATHLOS web page and the ATHLOS cohort profile …”

Section: Methodsmentioning

confidence: 99%

Stability of clinically relevant depression symptoms in old‐age across 11 cohorts: a multi‐state study

Torre‐Luque

Sánchez-Niubò

et al. 2019

Acta Psychiatr Scand

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Stability of clinically relevant depression symptoms in old-age across 11 cohorts: a multi-state study de la Torre-Luque A, de la Fuente J, Sanchez-Niubo A, et al. Stability of clinically relevant depression symptoms in old-age across 11 cohorts: a multi-state study.Aims: To study the temporal dynamics of depression symptom episodes in old-age and the related influence of risk factors. Methods: Data from 41 362 old adults (54.61% women; mean age = 75.30, SD = 6.20) from the Ageing Trajectories of Health -Longitudinal Opportunities and Synergies (ATHLOS) project were used. Depressive symptoms were followed over an 18-year period. A multi-state model, comprising three statuses (no depression, new clinically relevant episode of symptoms and episode persistence), was fitted. Multinomial regression was used to study the role of risk factors in status transition. Results: Almost 85% of participants showed no depression, but prevalence became lower over time (B = À0.25, P < 0.001). New episode point prevalence was over 5.30% with a significant probability of moving to persistence status (transition probability = 0.27). Episode persistence became evident in 9.86% of episode status transitions, with increasing rate over time (B = 0.54, P < 0.01). Loneliness was proven to be the strongest predictor of episode emergence (OR = 17.76) and persistence (OR = 5.93). Conclusions: The course of depression tends to become chronic and unremitting in old-age. This study may help to plan interventions to tackle symptom escalation and risk factor influence. Significant outcomes• Most of older adults did not show any episode of clinically relevant symptoms of depression over time.• Almost one in four older individuals who showed a first episode of symptoms showed either episode persistence or episode recurrence later in life.• Loneliness was the most relevant factor in predicting symptom episode emergence and persistence. Limitations• This is an epidemiological study focused on providing a coarse-grained picture of late-life depression symptom dynamics.• Despite our large sample from multiple countries across the globe, cultural issues were not controlled in this study.

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Cohort Profile: The Ageing Trajectories of Health – Longitudinal Opportunities and Synergies (ATHLOS) project

Cited by 40 publications

References 26 publications

The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies

The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies

Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts

Stability of clinically relevant depression symptoms in old‐age across 11 cohorts: a multi‐state study

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